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arXiv 2021年

作者： Li, Yong Lao, Lingjie Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. © 2021, CC BY-NC-SA.

关键词： Face recognition

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Mimicking synaptic plasticity based on TiO2/MgO film resistive switching device

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Materials Science in Semiconductor processing 2025年 197卷

作者： Pan, Jinyan Li, Jing He, Ruotong Huang, Qiao Li, Tiejun Yan, Lingsen Liu, Yan He, Hongyang Gao, Yunlong School of Ocean Information Engineering Jimei University Fujian Xiamen361021 China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Fujian Xiamen361021 China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Fujian Xiamen361102 China

The TiO2/MgO composite film was prepared via atomic layer deposition, exhibiting superior resistive switching (RS) performance compared to pure MgO film, including a higher resistance ratio (Roff/Ron) and enhanced retention. The Ag/TiO2/MgO/ITO device exhibited gradual RS characteristics, making it suitable for neuromorphic applications. The device also displayed excellent multilevel RS properties analogous to synaptic plasticity. When a series of positive or negative scanning voltages with progressively increasing amplitudes were applied, the resistance of the device switched and remained at correspondingly higher or lower states, effectively mimicking synaptic potentiation or depression in an amplitude-dependent manner. Furthermore, long-term potentiation and depression behaviors were observed under the time-dependent stimulation. As the pulse voltage amplitude increased, fewer pulses were required to set the device to a specific state. Conversely, lower voltage necessitated more pulses to facilitate Ag ions migration and conductive filament formation. This dynamic mimics synaptic weight modulation, where the evaluation of filaments over time corresponds to synaptic adjustment. Therefore, applying higher or lower amplitude voltages for longer or shorter durations aligns with the observation, where varying stimuli to the presynaptic membrane modulate synaptic weight, thereby strengthening (and weakening) the synaptic signal. The pulse amplitude and duration thresholds modulate the RS behavior of the composite TiO2/MgO film effectively mimic the learning and forgetting processes of human brain memory. © 2025

关键词： Composite films

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Star extraction by star sensors for daytime images affected by atmospheric turbulence

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Chinese Journal of Aeronautics 2025年

作者： Wanxiang GOU Yinhu ZHAN Chonghui LI Shuai TONG Yong ZHENG Yuan YANG Hanxu LI College of Geospatial Information Information Engineering University Zhengzhou 450000 China Key Laboratory of Smart Earth Beijing 100029 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou 450000 China Collaborative Innovation Center of BDS Navigation Application Technology Henan Province Zhengzhou 450000 China

Daytime star images captured by dedicated near-space star sensors are characterized by short exposures, high noise, and low Signal-to-Noise Ratios (SNRs). Such imaging is also affected by atmospheric turbulence, causing optical phenomena, such as scintillation, distortion, and jitter. This causes difficulty in recording high-precision star images during the daytime. This study proposes an adaptive star point extraction method based on dynamically predicting stars’ positions. First, it predicts the approximate position of stars based on the star catalog, sensor attitude, observation time, and other information, improving the extraction accuracy. Second, it employs a regional SNR sorting method that adaptively selects star images with higher SNRs, suppressing the scintillation effect and enhancing the SNR of star images. Third, depending on the star’s motion trajectory characteristics on the image plane, it utilizes the centroid smoothing method for extraction, thus overcoming the impact of star drift. Field experiments demonstrate that the proposed method can effectively overcome star scintillation, drift, and irregular imaging caused by atmospheric turbulence, achieving a 100% success rate. Moreover, the extraction accuracy improves by more than 80% compared to traditional adaptive methods, attaining a value of 0.05 pixels (0.5’’), thereby meeting the requirements of daytime astronomical attitude determination and positioning.

关键词： Astronomical navigation Star sensor Daytime star image Atmospheric turbulence Star extraction Centroid accuracy

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The Microsphere of Sodium Alginate-Chitosan-Pichia Kudriavzevii Enhanced Esterase Activity to Increase the Content of Esters in Baijiu Solid-State Fermentation

SSRN

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SSRN 2022年

作者： Huang, Jiaqing Qiao, Cailin Wang, Xinrou Gao, Yuwei Zhao, Jinsong Luo, Huibo Wang, Yongzhong Huo, Danqun Hou, Changjun Key Laboratory for Biorheological Science and Technology Ministry of Education College of Bioengineering Chongqing University Chongqing400044 China Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province College of Bioengineering Sichuan University of Science and Engineering 188 University Town Yi bin Sichuan 644000 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Chongqing University China

Herein, P. kudriavzevii cells were immobilized in a gel network formed by chitosan and sodium alginate to form sodium alginate/chitosan-P. kudriavzevii microspheres (AS/CS-PMs). Their structural characteristics and formation processes were characterized by SEM and FT-IR. The effects of synthesis conditions on the performance of microspheres were determined by single-factor experiments. Under the optimal conditions, the SA/CS-PMs could increase the amylase activity of the fermentation broth by 57.18%, the esterase activity by 66.13%, the ester content by 67.04%, and could be reused at least three times. Further research results indicated that the content of esters could be increased significantly in Baijiu solid-state fermentation with the SA/CS-PMs. In conclusion, the SA/CS-PMs could improve the ester production ability of P. kudriavzevii by increasing the esterase activity, which was a valuable exploration for the targeted synthesis of esters in the fermentation of Baijiu. © 2022, The Authors. All rights reserved.

关键词： Esters

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Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100080 China Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Research Center for Industries of the Future the School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. Codes will be available at https://***/IVIPLab/FIWHN. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Application research of general aircraft fault prognostic and health management technology 5th

Application research of general aircraft fault prognostic an...

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5th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2018

作者： Changsheng, Liu Changyun, Li Min, Liu Ying, Cheng Jie, Huang Changsha Aeronautical Vocational and Technical College ChangshaHunan410014 China Hunan Key Laboratory of Intelligent Information Perception and Processing Technology ZhuzhouHunan412007 China

ISBN: (纸本)9783030037659

General aircrafts are very complicated to analyze from the perspective of structure and system, which means the maintenance task is very heavy. The traditional "broken-then- repair" and "planned repair" methods have serious shortcomings in dealing with the ever-changing new situation. "Maintenance depending on the situation" and "predictive maintenance" will nip the fault in the bud and become the direction of future system maintenance strategy development. This research studies three key technologies: general aircraft intelligent monitoring technology, general aircraft health assessment and prediction method based on multi-source big data fusion, and general aircraft operation and maintenance process visualization evolution simulation technology. Built on these technologies, a General Aviation Health Supervision platform is developed. This supervision platform is of great significance to improve the safety and reliability of general aviation aircraft, reduce operation and maintenance costs, and promote the development of the local navigation industry. The research outcome is tested on the Ararat SA60L light sport aircraft manufactured by Hunan Shanhe technology Co., Ltd. The test confirms that the general aviation health supervision platform, successfully provides real-time, systematic and intelligent solution for the monitoring and health supervision of general aviation aircrafts. It is expected that the new platform will create a revenue of more than 50 million yuan in the first three years of commercialization, with an annual growth rate of over 20%. © Springer Nature Switzerland AG 2019.

关键词： Health

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Retraction Notice to "Reverse iontophoresis generated by porous microneedles produces an electroosmotic flow for glucose determination" [Talanta 267 (2024) 125156]

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Talanta 2025年 283卷 127102页

作者： Qi-Yao He Jia-Hao Zhao Shi-Ming Du De-Gui Li Zhi-Wei Luo Xue-Qiu You Jing Liu School of Navigation Jimei University Xiamen 361021 China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China. School of Marine Information Engineering Jimei University Xiamen 361021 China. School of Mechanical and Automotive Engineering Xiamen University of Technology Xiamen 361024 China. School of Marine Information Engineering Jimei University Xiamen 361021 China

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/about/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The authors have plagiarized part of a paper that had already appeared in Nat. Commun.12, (2021) 658, https://***/10.1038/s41467-021-20948-4. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

引用

International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

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Towards explainable multi-party learning: A contrastive knowledge sharing framework

arXiv

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arXiv 2021年

作者： Gao, Yuan Li, Jiawei Gong, Maoguo Xie, Yu Qin, A.K. The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Shaanxi Province Xi'an710071 China The Key Laboratory of Computational Intelligence Chinese Information Processing Ministry of Education Shanxi University Taiyuan030006 China The department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approaches are confronted with obstacles such as system heterogeneity, statistical heterogeneity, and incentive design. How to deal with these challenges and further improve the efficiency and performance of multi-party learning has become an urgent problem to be solved. In this paper, we propose a novel contrastive multi-party learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogize multiparty learning as a many-to-one knowledge sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge management

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